公开(公告)号：US11409347B2

公开(公告)日：2022-08-09

申请号：US16284064

申请日：2019-02-25

Applicant: Hefei University of Technology

Inventor： Kaile Zhou ,  Zhifeng Guo ,  Shanlin Yang ,  Pengtao Li ,  Lulu Wen ,  Xinhui Lu

IPC: G06F1/28 ,  G06N20/00 ,  G06N7/00

Abstract: The disclosure provides a method, a system and a storage medium for predicting power load probability density based on deep learning. The method comprises: S101, collecting power load data of a user, meteorological data and air quality data in a preset historical time period, and dividing the collected data into a training set and a test set; S102, determining a deep learning model for predicting power load; S103, inputting the test set into the deep learning model for predicting power load, and obtaining power load prediction data of the user at different quantile points in a third time interval; S104, performing kernel density estimation and obtaining a probability density curve of the power load of the user in the third time interval.

公开(公告)号：US10571871B2

公开(公告)日：2020-02-25

申请号：US16056045

申请日：2018-08-06

Applicant: Hefei University of Technology

Inventor： Qiang Zhang ,  Shanlin Yang ,  Zhanglin Peng ,  Jun Pei ,  Xiaonong Lu

IPC: G05B13/02 ,  F15B21/08 ,  G06N7/00 ,  G05B19/18

Abstract: The present invention discloses an automatic loading system and method for service functions of a hydraulic machine. The loading system includes an acquiring portion for acquiring i service function component chains of a hydraulic machine, a determining portion for determining a demand sequence of the service function component chains used by a user within each preset time period in a measuring cycle based on the acquired service function component chains and a deciding portion for automatically loading a service function of the hydraulic machine in a next moment according to the demand sequence based on a current input operation command of the user. By adopting the automatic loading system or method provided by the present invention, response speed of intelligent services of the hydraulic machine can be improved.

公开(公告)号：US10211851B2

公开(公告)日：2019-02-19

申请号：US15947857

申请日：2018-04-08

Applicant: Hefei University of Technology

Inventor： Kaile Zhou ,  Lulu Wen ,  Shanlin Yang ,  Xinhui Lu ,  Zhen Shao ,  Li Sun

IPC: H03M7/00 ,  H03M7/30 ,  G01R22/06 ,  G01D4/00 ,  H04L29/06

Abstract: The present invention relates to a method and a system for compressing data from a smart meter. The method comprises: LZ-encoding electricity load data collected by the smart meter whenever the smart meter collects the electricity load data; storing the LZ-encoded electricity load data in a temporary database through a smart grid communication channel; reading the electricity load data from the temporary database every preset second duration, wherein the read electricity load data is electricity load data stored in the temporary database within the second duration before a corresponding reading time point; and LZ-decoding the read electricity load data, SAX-compressing the LZ-decoded electricity load data, and storing the SAX-compressed electricity load data in a data center. The present invention has high compression rate, reduces the transmission burden for communication lines and storage burden for the data center, and improves the efficiency of smart electricity data analysis and mining.

公开(公告)号：US10140875B1

公开(公告)日：2018-11-27

申请号：US15953881

申请日：2018-04-16

Applicant: Hefei University of Technology

Inventor： He Luo ,  Guoqiang Wang ,  Zhengzheng Liang ,  Xiaoxuan Hu ,  Shanlin Yang ,  Huawei Ma ,  Wei Xia ,  Peng Jin ,  Moning Zhu ,  Yanqiu Niu ,  Xiang Fang

IPC: G08G5/00 ,  G06Q10/04 ,  G06N3/12 ,  B64C39/02

CPC classification number: G08G5/0043 ,  B64C39/024 ,  B64C2201/141 ,  G06N3/126 ,  G06Q10/047 ,  G08G5/0013 ,  G08G5/0026 ,  G08G5/0039

Abstract: The embodiments of the present invention disclose a method and apparatus for joint optimization of multi-UAV task assignment and path planning. The method comprises: obtaining the location information of a plurality of UAVs and a plurality of target points, the dispersion of groundspeed course angle, and motion parameters of each UAV and wind field; constructing an initial population based on the location information, the dispersion of groundspeed course angle and a preset genetic algorithm; determining the flight status of each UAV and the flight time taken by each UAV to complete a path segment of the corresponding Dubins flight path based on the initial population and the motion parameters, obtaining the total time taken by all the UAVs corresponding to each chromosome to complete the task based on the flight time of the path segment; and subjecting the chromosomes in the initial population to crossover and mutation based on the genetic algorithm and, when a predetermined number of iterations is reached, selecting the optimal Dubins flight path as the joint optimization result. In the embodiments of the present invention, the UAV flight path planning problem is combined with the actual flight environment of the UAV, so that the optimal flight path obtained is superior to the solution in which the UAV speed is constant.

公开(公告)号：US10124675B2

公开(公告)日：2018-11-13

申请号：US15794989

申请日：2017-10-26

Applicant: Hefei University of Technology

Inventor： Xiaonong Lu ,  Qiang Zhang ,  Wanying Wang ,  Anning Wang ,  Shanlin Yang ,  Minglun Ren ,  Zhanglin Peng

IPC: B60L3/12 ,  B60L11/18

Abstract: The present invention relates to a method and device for on-line prediction of remaining driving mileage of an electric vehicle. The method comprises: acquiring in-transit data and driving environment data of the electric vehicle which is driving; calculating the power consumption per mileage of the electric vehicle in the current case by using the in-transit data and the driving environment data in combination with a power consumption rate data model; predicting the remaining driving mileage of the electric vehicle based on the power consumption per mileage. The device provided by the present invention is implemented on the basis of the method above. The prediction result of the present invention is more accurate, to avoid the problem that the power is exhausted due to exceeding the mileage expected by a user so that the electric vehicle cannot continue to drive, thereby improving the driving experience of the user.